wtf section

wtf stands for what to fit. This section parametrizes the failed cavities, as well as how they are fixed.

k out of n method

Entry	Type	Description	Mandatory?	Allowed values
failed	list	Index/name of failed cavities. Must be a list[list[int]] or list[list[str]].	✅
history_kwargs	dict	kwargs for the OptimizationHistory.	❌
id_nature	str	Indicates if failed is element index/cavity index/name, element or cavity or name.	✅	(‘element’, ‘cavity’, ‘name’)
objective_preset	str	Objectives for the optimisation algorithm.	✅	(‘EnergyMismatch’, ‘EnergyPhaseMismatch’, ‘EnergySeveralMismatches’, ‘EnergySyncPhaseMismatch’, ‘experimental’, ‘rephased_ADS’, ‘simple_ADS’, ‘sync_phase_as_objective_ADS’)
optimisation_ algorithm	str	Name of optimisation algorithm.	✅	(‘bayesian_optimization’, ‘differential_evolution’, ‘downhill_simplex’, ‘downhill_simplex_penalty’, ‘experimental’, ‘explorator’, ‘least_squares’, ‘least_squares_penalty’, ‘nelder_mead’, ‘nelder_mead_penalty’, ‘nsga’)
optimisation_ algorithm_kwargs	dict	Keyword arguments passed to the optimisation algorithm.	❌
strategy	str	How compensating cavities are selected.	✅	(‘k out of n’, ‘l neighboring lattices’, ‘global’, ‘global_downstream’, ‘manual’)
tie_politics	str	How to select the compensating elements when several are equidistant to the failure.	❌	(‘upstream first’, ‘downstream first’)
shift	int	Distance increase for downstream elements (shift < 0) or upstream elements (shift > 0). Used to have a window of compensating cavities which is not centered around the failed elements.	❌
k	int	Number of compensating cavities per failed cavity.	✅

l neighboring lattices method

Entry	Type	Description	Mandatory?	Allowed values
failed	list	Index/name of failed cavities. Must be a list[list[int]] or list[list[str]].	✅
history_kwargs	dict	kwargs for the OptimizationHistory.	❌
id_nature	str	Indicates if failed is element index/cavity index/name, element or cavity or name.	✅	(‘element’, ‘cavity’, ‘name’)
objective_preset	str	Objectives for the optimisation algorithm.	✅	(‘EnergyMismatch’, ‘EnergyPhaseMismatch’, ‘EnergySeveralMismatches’, ‘EnergySyncPhaseMismatch’, ‘experimental’, ‘rephased_ADS’, ‘simple_ADS’, ‘sync_phase_as_objective_ADS’)
optimisation_ algorithm	str	Name of optimisation algorithm.	✅	(‘bayesian_optimization’, ‘differential_evolution’, ‘downhill_simplex’, ‘downhill_simplex_penalty’, ‘experimental’, ‘explorator’, ‘least_squares’, ‘least_squares_penalty’, ‘nelder_mead’, ‘nelder_mead_penalty’, ‘nsga’)
optimisation_ algorithm_kwargs	dict	Keyword arguments passed to the optimisation algorithm.	❌
strategy	str	How compensating cavities are selected.	✅	(‘k out of n’, ‘l neighboring lattices’, ‘global’, ‘global_downstream’, ‘manual’)
tie_politics	str	How to select the compensating elements when several are equidistant to the failure.	❌	(‘upstream first’, ‘downstream first’)
shift	int	Distance increase for downstream elements (shift < 0) or upstream elements (shift > 0). Used to have a window of compensating cavities which is not centered around the failed elements.	❌
l	int	Number of compensating lattices per failed cavity.	✅
min_number_of_ cavities_in_lattice	int	Minimum number of compensating cavities in the lattice; when a lattice does not reach this number, we use it anyway for compensation, but we also take another lattice. Designed to remove the lattices that do not have any cavity.	❌

Manual association of failed / compensating cavities

If you want to manually associate each failed cavity with its compensating cavities:

Entry	Type	Description	Mandatory?	Allowed values
failed	list	Index/name of failed cavities. Must be a list[list[int]] or list[list[str]].	✅
history_kwargs	dict	kwargs for the OptimizationHistory.	❌
id_nature	str	Indicates if failed is element index/cavity index/name, element or cavity or name.	✅	(‘element’, ‘cavity’, ‘name’)
objective_preset	str	Objectives for the optimisation algorithm.	✅	(‘EnergyMismatch’, ‘EnergyPhaseMismatch’, ‘EnergySeveralMismatches’, ‘EnergySyncPhaseMismatch’, ‘experimental’, ‘rephased_ADS’, ‘simple_ADS’, ‘sync_phase_as_objective_ADS’)
optimisation_ algorithm	str	Name of optimisation algorithm.	✅	(‘bayesian_optimization’, ‘differential_evolution’, ‘downhill_simplex’, ‘downhill_simplex_penalty’, ‘experimental’, ‘explorator’, ‘least_squares’, ‘least_squares_penalty’, ‘nelder_mead’, ‘nelder_mead_penalty’, ‘nsga’)
optimisation_ algorithm_kwargs	dict	Keyword arguments passed to the optimisation algorithm.	❌
strategy	str	How compensating cavities are selected.	✅	(‘k out of n’, ‘l neighboring lattices’, ‘global’, ‘global_downstream’, ‘manual’)
failed	list	Index/name of failed cavities. Must be a list[list[list[int]]] or list[list[list[str]]].	✅
compensating_manual	list	Index/name of compensating cavities cavities. Must be a list[list[list[int]]] or list[list[list[str]]]. The number of:class:.FaultScenarios ( length of most outer list) must match failed. The number of groups of compensating cavities (second level) must match failed.	✅

Example

# Indexes are cavity indexes
idx = "cavity"
failed = [
   [0, 1],       # First simulation first cryomodule is down
   [0],          # Second simulation only first cavity is down
   [1, 45]       # Third simulation second and 46th cavity are down
]

Optimisation algorithms

Here are mappings of optimisation_algorithm key to actual OptimisationAlgorithm. Check the documentation of the optimisation algorithm you want to use, in particular if you want to tune it using optimisation_algorithm_kwargs key.

Optimisation algorithm	Corresponding keys
BayesianOptimizationLW	bayesian_optimization, experimental
DifferentialEvolution	differential_evolution
DownhillSimplex	downhill_simplex, nelder_mead
DownhillSimplexPenalty	downhill_simplex_penalty, nelder_mead_penalty
Explorator	explorator
LeastSquares	least_squares
LeastSquaresPenalty	least_squares_penalty
NSGA	nsga
SimulatedAnnealing	simulated_annealing